


'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class   UpperCAmelCase_    (a_   ,						a_   ,						a_   ,						unittest.TestCase    ):





    """simple docstring"""






    lowerCamelCase    :   Tuple					=     StableDiffusionInstructPixaPixPipeline
    lowerCamelCase    :   int					=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
    lowerCamelCase    :   List[str]					=     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    lowerCamelCase    :   Any					=     IMAGE_TO_IMAGE_IMAGE_PARAMS
    lowerCamelCase    :   Optional[int]					=     IMAGE_TO_IMAGE_IMAGE_PARAMS







    def  lowercase_							(				self      )							->	List[str]:
         torch.manual_seed(0      )
         __lowerCamelCase  :      int														=						UNetaDConditionModel(
             block_out_channels=(32, 64)     ,	layers_per_block=2     ,	sample_size=32     ,	in_channels=8     ,	out_channels=4     ,	down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')     ,	up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')     ,	cross_attention_dim=32     ,	)
         __lowerCamelCase  :      Any														=						PNDMScheduler(skip_prk_steps=UpperCamelCase_      )
         torch.manual_seed(0      )
         __lowerCamelCase  :      Union[str, Any]														=						AutoencoderKL(
             block_out_channels=[32, 64]     ,	in_channels=3     ,	out_channels=3     ,	down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']     ,	up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']     ,	latent_channels=4     ,	)
         torch.manual_seed(0      )
         __lowerCamelCase  :      List[str]														=						CLIPTextConfig(
             bos_token_id=0     ,	eos_token_id=2     ,	hidden_size=32     ,	intermediate_size=37     ,	layer_norm_eps=1E-05     ,	num_attention_heads=4     ,	num_hidden_layers=5     ,	pad_token_id=1     ,	vocab_size=10_00     ,	)
         __lowerCamelCase  :      int														=						CLIPTextModel(UpperCamelCase_      )
         __lowerCamelCase  :      List[Any]														=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

         __lowerCamelCase  :      str														=						{
             'unet': unet,
             'scheduler': scheduler,
             'vae': vae,
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'safety_checker': None,
             'feature_extractor': None,
         }
         return components







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=0      )							->	List[Any]:
         __lowerCamelCase  :      List[Any]														=						floats_tensor((1, 3, 32, 32)     ,	rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
         __lowerCamelCase  :      Optional[Any]														=						image.cpu().permute(0     ,	2     ,	3     ,	1      )[0]
         __lowerCamelCase  :      Tuple														=						Image.fromarray(np.uinta(UpperCamelCase_      )      ).convert('RGB'      )
         if str(UpperCamelCase_      ).startswith('mps'      ):
              __lowerCamelCase  :      List[str]														=						torch.manual_seed(UpperCamelCase_      )
         else:
              __lowerCamelCase  :      str														=						torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )
         __lowerCamelCase  :      Dict														=						{
             'prompt': 'A painting of a squirrel eating a burger',
             'image': image,
             'generator': generator,
             'num_inference_steps': 2,
             'guidance_scale': 6.0,
             'image_guidance_scale': 1,
             'output_type': 'numpy',
         }
         return inputs







    def  lowercase_							(				self      )							->	Dict:
         __lowerCamelCase  :      Optional[int]														=						'cpu'  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase  :      List[str]														=						self.get_dummy_components()
         __lowerCamelCase  :      int														=						StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_      )
         __lowerCamelCase  :      Dict														=						sd_pipe.to(UpperCamelCase_      )
         sd_pipe.set_progress_bar_config(disable=UpperCamelCase_      )

         __lowerCamelCase  :      List[str]														=						self.get_dummy_inputs(UpperCamelCase_      )
         __lowerCamelCase  :      Optional[Any]														=						sd_pipe(**UpperCamelCase_      ).images
         __lowerCamelCase  :      Optional[Any]														=						image[0, -3:, -3:, -1]
         assert image.shape == (1, 32, 32, 3)
         __lowerCamelCase  :      Any														=						np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3







    def  lowercase_							(				self      )							->	str:
         __lowerCamelCase  :      List[str]														=						'cpu'  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase  :      int														=						self.get_dummy_components()
         __lowerCamelCase  :      int														=						StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_      )
         __lowerCamelCase  :      Tuple														=						sd_pipe.to(UpperCamelCase_      )
         sd_pipe.set_progress_bar_config(disable=UpperCamelCase_      )

         __lowerCamelCase  :      Union[str, Any]														=						self.get_dummy_inputs(UpperCamelCase_      )
         __lowerCamelCase  :      int														=						'french fries'
         __lowerCamelCase  :      Union[str, Any]														=						sd_pipe(**UpperCamelCase_     ,	negative_prompt=UpperCamelCase_      )
         __lowerCamelCase  :      Optional[int]														=						output.images
         __lowerCamelCase  :      Union[str, Any]														=						image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         __lowerCamelCase  :      Tuple														=						np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3







    def  lowercase_							(				self      )							->	List[str]:
         __lowerCamelCase  :      str														=						'cpu'  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase  :      Tuple														=						self.get_dummy_components()
         __lowerCamelCase  :      Optional[Any]														=						StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_      )
         __lowerCamelCase  :      str														=						sd_pipe.to(UpperCamelCase_      )
         sd_pipe.set_progress_bar_config(disable=UpperCamelCase_      )

         __lowerCamelCase  :      Dict														=						self.get_dummy_inputs(UpperCamelCase_      )
         __lowerCamelCase  :      Optional[int]														=						[inputs['prompt']] * 2

         __lowerCamelCase  :      Optional[int]														=						np.array(inputs['image']      ).astype(np.floataa      ) / 2_5_5.0
         __lowerCamelCase  :      Tuple														=						torch.from_numpy(UpperCamelCase_      ).unsqueeze(0      ).to(UpperCamelCase_      )
         __lowerCamelCase  :      Dict														=						image / 2 + 0.5
         __lowerCamelCase  :      str														=						image.permute(0     ,	3     ,	1     ,	2      )
         __lowerCamelCase  :      List[str]														=						image.repeat(2     ,	1     ,	1     ,	1      )

         __lowerCamelCase  :      Any														=						sd_pipe(**UpperCamelCase_      ).images
         __lowerCamelCase  :      Dict														=						image[-1, -3:, -3:, -1]

         assert image.shape == (2, 32, 32, 3)
         __lowerCamelCase  :      List[str]														=						np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3







    def  lowercase_							(				self      )							->	Optional[int]:
         __lowerCamelCase  :      Tuple														=						'cpu'  # ensure determinism for the device-dependent torch.Generator
         __lowerCamelCase  :      Tuple														=						self.get_dummy_components()
         __lowerCamelCase  :      Tuple														=						EulerAncestralDiscreteScheduler(
             beta_start=0.0_0_0_8_5     ,	beta_end=0.0_1_2     ,	beta_schedule='scaled_linear'      )
         __lowerCamelCase  :      str														=						StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_      )
         __lowerCamelCase  :      Any														=						sd_pipe.to(UpperCamelCase_      )
         sd_pipe.set_progress_bar_config(disable=UpperCamelCase_      )

         __lowerCamelCase  :      str														=						self.get_dummy_inputs(UpperCamelCase_      )
         __lowerCamelCase  :      int														=						sd_pipe(**UpperCamelCase_      ).images
         __lowerCamelCase  :      Dict														=						image[0, -3:, -3:, -1]

         __lowerCamelCase  :      int														=						[round(UpperCamelCase_     ,	4      ) for x in image_slice.flatten().tolist()]
         print(','.join([str(UpperCamelCase_      ) for x in slice]      )      )

         assert image.shape == (1, 32, 32, 3)
         __lowerCamelCase  :      Any														=						np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3







    def  lowercase_							(				self      )							->	str:
         super().test_inference_batch_single_identical(expected_max_diff=3E-3      )







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      Optional[int]														=						self.get_dummy_components()
         __lowerCamelCase  :      Tuple														=						StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_      )
         __lowerCamelCase  :      List[str]														=						VaeImageProcessor(do_resize=UpperCamelCase_     ,	do_normalize=UpperCamelCase_      )
         __lowerCamelCase  :      Dict														=						pipe.to(UpperCamelCase_      )
         pipe.set_progress_bar_config(disable=UpperCamelCase_      )

         __lowerCamelCase  :      Optional[Any]														=						pipe(**self.get_dummy_inputs_by_type(UpperCamelCase_     ,	input_image_type='pt'      )      )[0]

         __lowerCamelCase  :      List[str]														=						components['vae']
         __lowerCamelCase  :      Optional[int]														=						self.get_dummy_inputs_by_type(UpperCamelCase_     ,	input_image_type='pt'      )

         for image_param in self.image_latents_params:
              if image_param in inputs.keys():
                   __lowerCamelCase  :      Tuple														=						vae.encode(inputs[image_param]      ).latent_dist.mode()

         __lowerCamelCase  :      Optional[int]														=						pipe(**UpperCamelCase_      )[0]

         __lowerCamelCase  :      Any														=						np.abs(out - out_latents_inputs      ).max()
         self.assertLess(UpperCamelCase_     ,	1E-4     ,	'passing latents as image input generate different result from passing image'      )







@slow
@require_torch_gpu
class   UpperCAmelCase_    (unittest.TestCase    ):





    """simple docstring"""






    def  lowercase_							(				self      )							->	List[str]:
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_=0      )							->	List[Any]:
         __lowerCamelCase  :      Tuple														=						torch.manual_seed(UpperCamelCase_      )
         __lowerCamelCase  :      int														=						load_image(
             'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'      )
         __lowerCamelCase  :      Union[str, Any]														=						{
             'prompt': 'turn him into a cyborg',
             'image': image,
             'generator': generator,
             'num_inference_steps': 3,
             'guidance_scale': 7.5,
             'image_guidance_scale': 1.0,
             'output_type': 'numpy',
         }
         return inputs







    def  lowercase_							(				self      )							->	Union[str, Any]:
         __lowerCamelCase  :      Optional[Any]														=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
             'timbrooks/instruct-pix2pix'     ,	safety_checker=UpperCamelCase_      )
         pipe.to(UpperCamelCase_      )
         pipe.set_progress_bar_config(disable=UpperCamelCase_      )
         pipe.enable_attention_slicing()

         __lowerCamelCase  :      int														=						self.get_inputs()
         __lowerCamelCase  :      Dict														=						pipe(**UpperCamelCase_      ).images
         __lowerCamelCase  :      Tuple														=						image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase  :      Any														=						np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5]      )

         assert np.abs(expected_slice - image_slice      ).max() < 1E-3







    def  lowercase_							(				self      )							->	Dict:
         __lowerCamelCase  :      str														=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
             'timbrooks/instruct-pix2pix'     ,	safety_checker=UpperCamelCase_      )
         __lowerCamelCase  :      Union[str, Any]														=						LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
         pipe.to(UpperCamelCase_      )
         pipe.set_progress_bar_config(disable=UpperCamelCase_      )
         pipe.enable_attention_slicing()

         __lowerCamelCase  :      str														=						self.get_inputs()
         __lowerCamelCase  :      Optional[int]														=						pipe(**UpperCamelCase_      ).images
         __lowerCamelCase  :      Tuple														=						image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase  :      int														=						np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1]      )

         assert np.abs(expected_slice - image_slice      ).max() < 1E-3







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      Union[str, Any]														=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
             'timbrooks/instruct-pix2pix'     ,	safety_checker=UpperCamelCase_      )
         __lowerCamelCase  :      Dict														=						DDIMScheduler.from_config(pipe.scheduler.config      )
         pipe.to(UpperCamelCase_      )
         pipe.set_progress_bar_config(disable=UpperCamelCase_      )
         pipe.enable_attention_slicing()

         __lowerCamelCase  :      List[str]														=						self.get_inputs()
         __lowerCamelCase  :      List[str]														=						pipe(**UpperCamelCase_      ).images
         __lowerCamelCase  :      Any														=						image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase  :      List[Any]														=						np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3]      )

         assert np.abs(expected_slice - image_slice      ).max() < 1E-3







    def  lowercase_							(				self      )							->	Optional[int]:
         __lowerCamelCase  :      List[str]														=						0

         def callback_fn(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ) -> None:
              __lowerCamelCase  :      Optional[int]														=						True
              nonlocal number_of_steps
              number_of_steps += 1
              if step == 1:
                   __lowerCamelCase  :      str														=						latents.detach().cpu().numpy()
                   assert latents.shape == (1, 4, 64, 64)
                   __lowerCamelCase  :      Any														=						latents[0, -3:, -3:, -1]
                   __lowerCamelCase  :      str														=						np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3]      )

                   assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5E-2
              elif step == 2:
                   __lowerCamelCase  :      Optional[Any]														=						latents.detach().cpu().numpy()
                   assert latents.shape == (1, 4, 64, 64)
                   __lowerCamelCase  :      Union[str, Any]														=						latents[0, -3:, -3:, -1]
                   __lowerCamelCase  :      str														=						np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5]      )

                   assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5E-2

         __lowerCamelCase  :      Dict														=						False

         __lowerCamelCase  :      Any														=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
             'timbrooks/instruct-pix2pix'     ,	safety_checker=UpperCamelCase_     ,	torch_dtype=torch.floataa      )
         __lowerCamelCase  :      int														=						pipe.to(UpperCamelCase_      )
         pipe.set_progress_bar_config(disable=UpperCamelCase_      )
         pipe.enable_attention_slicing()

         __lowerCamelCase  :      str														=						self.get_inputs()
         pipe(**UpperCamelCase_     ,	callback=UpperCamelCase_     ,	callback_steps=1      )
         assert callback_fn.has_been_called
         assert number_of_steps == 3







    def  lowercase_							(				self      )							->	str:
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         __lowerCamelCase  :      Dict														=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
             'timbrooks/instruct-pix2pix'     ,	safety_checker=UpperCamelCase_     ,	torch_dtype=torch.floataa      )
         __lowerCamelCase  :      int														=						pipe.to(UpperCamelCase_      )
         pipe.set_progress_bar_config(disable=UpperCamelCase_      )
         pipe.enable_attention_slicing(1      )
         pipe.enable_sequential_cpu_offload()

         __lowerCamelCase  :      List[Any]														=						self.get_inputs()
         __lowerCamelCase  :      Optional[Any]														=						pipe(**UpperCamelCase_      )

         __lowerCamelCase  :      Dict														=						torch.cuda.max_memory_allocated()
         # make sure that less than 2.2 GB is allocated
         assert mem_bytes < 2.2 * 10**9







    def  lowercase_							(				self      )							->	str:
         __lowerCamelCase  :      List[Any]														=						self.get_inputs()
         # resize to resolution that is divisible by 8 but not 16 or 32
         __lowerCamelCase  :      Optional[int]														=						inputs['image'].resize((5_04, 5_04)      )

         __lowerCamelCase  :      int														=						'timbrooks/instruct-pix2pix'
         __lowerCamelCase  :      Optional[int]														=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
             UpperCamelCase_     ,	safety_checker=UpperCamelCase_     ,	)
         pipe.to(UpperCamelCase_      )
         pipe.set_progress_bar_config(disable=UpperCamelCase_      )
         pipe.enable_attention_slicing()

         __lowerCamelCase  :      str														=						pipe(**UpperCamelCase_      )
         __lowerCamelCase  :      List[Any]														=						output.images[0]

         __lowerCamelCase  :      str														=						image[2_55:2_58, 3_83:3_86, -1]

         assert image.shape == (5_04, 5_04, 3)
         __lowerCamelCase  :      Dict														=						np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 5E-3














"""simple docstring"""
import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property



@unittest.skipUnless(os.path.exists(a_)							,	"""Tatoeba directory does not exist.""")
class        UpperCamelCase__ (				unittest.TestCase):







    """simple docstring"""







    @cached_property
    def 				a__			(		self :	int ):





           '''simple docstring'''

           __magic_name__      				=    tempfile.mkdtemp()
           return TatoebaConverter(save_dir=UpperCamelCase_ )







    @slow
    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           self.resolver.convert_models(['heb-eng'] )







    @slow
    def 				a__			(		self :	Tuple ):





           '''simple docstring'''

           __magic_name__				,  __magic_name__      				=    self.resolver.write_model_card('opus-mt-he-en'				,    dry_run=UpperCamelCase_ )
           assert mmeta["long_pair"] == "heb-eng"